ArticleScientific reports2019
A PET Radiomics Model to Predict Refractory Mediastinal Hodgkin Lymphoma.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 3 syntheses or guidelines pooled it, 101 citations in OpenAlex.
- Prognostic role of PET/CT volumetric parameters in paediatric Hodgkin lymphoma: A systematic review and expert recommendations from the International SEARCH for CAYAHL Group.British journal of haematology · 2026Pooled it
- Baseline PET/CT imaging parameters for prediction of treatment outcome in Hodgkin and diffuse large B cell lymphoma: a systematic review.European journal of nuclear medicine and molecular imaging · 2021Pooled it
- Current status and quality of radiomics studies in lymphoma: a systematic review.European radiology · 2020Pooled it
- A deep learning framework for lesion-level treatment response prediction in hodgkin lymphoma using PET/CT tensor radiomics.EJNMMI physics · 2026Article
- Non-Hodgkin's lymphoma classification using 3D radiomics machine learning models for precision imaging in oncology.BMC medical imaging · 2025Article
- A proof of concept study ofScientific reports · 2025Article
- Radiomics based onQuantitative imaging in medicine and surgery · 2025Article
- The Role of Artificial Intelligence and Radiomics in the Management of Lymphomas by PET/CT: The Clairvoyance in Clinic.Cancer management and research · 2025Review
- Review
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- Semiquantitative 2-[Frontiers in medicine · 2024Review
- Clinical application ofJapanese journal of radiology · 2024Review
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
- Prognostic model using 18F-FDG PET radiomics predicts progression-free survival in relapsed/refractory Hodgkin lymphoma.Blood advances · 2023Article
- BaselineDiagnostics (Basel, Switzerland) · 2023Article
- Combination of FDG PET/CT Radiomics and Clinical Parameters for Outcome Prediction in Patients with Hodgkin's Lymphoma.Cancers · 2023Article
- High-risk stage IIB Hodgkin lymphoma treated in the H10 and AHL2011 trials: total metabolic tumor volume is a useful risk factor to stratify patients at baseline.Haematologica · 2022Article
- CT radiomics to predict Deauville score 4 positive and negative Hodgkin lymphoma manifestations.Scientific reports · 2022Article
- Classical Hodgkin Lymphoma: A Joint Clinical and PET Model to Predict Poor Responders at Interim Assessment.Diagnostics (Basel, Switzerland) · 2022Article
- The Impact of Semiautomatic Segmentation Methods on Metabolic Tumor Volume, Intensity, and Dissemination Radiomics inJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
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Authors and funding
21 authors at 1 institution in 1 country.
Funding
Abstract
First-order radiomic features, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), are associated with disease progression in early-stage classical Hodgkin lymphoma (HL). We hypothesized that a model incorporating first- and second-order radiomic features would more accurately predict outcome than MTV or TLG alone. We assessed whether radiomic features extracted from baseline PET scans predicted relapsed or refractory disease status in a cohort of 251 patients with stage I-II HL who were managed at a tertiary cancer center. Models were developed and tested using a machine-learning algorithm. Features extracted from mediastinal sites were highly predictive of primary refractory disease. A model incorporating 5 of the most predictive features had an area under the curve (AUC) of 95.2% and total error rate of 1.8%. By comparison, the AUC was 78% for both MTV and TLG and was 65% for maximum standardize uptake value (SUV
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.